Heuristic Analysis Research Articles

СИСТЕМИ АВТОНОМНОГО ТЕПЛОЗАБЕЗПЕЧЕННЯ БУДІВЕЛЬ

The critical energy infrastructure plays an important role in ensuring the energy security of the state, creating conditions for life of population and development of national economy. The work examines the problems of developing and designing an autonomous energy supply system as a component of critical energy infrastructure, it is necessary to analyze the functions of individual components of the system and the criteria that determine the energy efficiency of the system as a whole.A sufficient number of scientific papers are devoted to the analysis of research and publications on the functional analysis of distributed energy systems, in particular, the formation of the optimal structure of the local electric power system based on the assessment of the degree of proximity of the location of objects with the use of cluster analysis for modeling systems based on the Microgrid concept is considered.The purpose of the article is to develop the concept of an autonomous local energy supply system as a component of critical energy infrastructure in order to increase the level of energy security of the country.The transformation of the existing system of centralized heat supply (or its addition) is possible with the use of combined production of heat and electricity in autonomous local energy supply systems using cogeneration technology on modern high-efficiency equipment and the widespread use of renewable energy sources (RES) in accordance with the requirements of the IV energy transition. This approach is harmonized with project development of the EU – Europen Technology Platform SmartGrid energy supply platform based on distributed generation and Smart technologies.For the analysis of schematic solutions of the structure of the autonomous local energy supply system, the heuristic analysis technique was applied, which made it possible to optimize the management of energy resource generation processes, in the form of components: technical means (TK), informational (IS) and mathematical (MZ) support.The paper provides a theoretical analysis of an autonomous heat supply system based on the principles of process mod-eling when using an automated control and energy monitoring system with self-learning elements.

Development, validation, and usability testing of prototype mobile application for oral health promotion during pregnancy in India.

Oral diseases are a silent epidemic. The objectives of the study were to develop, validate, and assess the usability of an oral health prototype mobile application for oral health promotion among pregnant women in India. A cross-sectional study was conducted in Tertiary Care Hospital in Delhi, India, after obtaining Ethical Clearance from the Institutional Ethical Committee Board. The study was conducted in three phases: development of the prototype app, its validation, followed by usability testing of the app. Mobile app was validated by 30 pregnant women and 30 subject experts using Heuristic Analysis Scale and usability testing by 30 pregnant women based on System Usability Scale (SUS). Data were analyzed with IBM SPSS version 21.0. Majority (over 90%) of pregnant women and subject experts strongly acknowledged that the app educated the users using positive motivation strategies, instilling comprehensive knowledge and faced no issues with the appropriate functionality of the app. The prototype app scored 73.75 on SUS, indicating high usability. This study holistically explored various dimensions of oral health care in pregnant women. Its novelty is proven by the fact that the content of the prototype application has been phase wise developed and validated by pregnant women and subject experts. Usability testing of the app indicated its high acceptability and ease of use among pregnant women in India.

МОДЕЛЮВАННЯ УКРАЇНСЬКОГО НАУКОВОГО ПРОСТОРУ НА ОСНОВІ АНАЛІЗУ ДАНИХ ПРО ЗАХИСТ ДИСЕРТАЦІЙ

Background. The paper considers the concept of scientific space and the peculiarities of its formation in Ukraine. Methods. Methods of heuristic analysis of weakly-structured information are used. Results. The structure of information about defended theses was studied. Heuristics, which, firstly, contribute to the structuring of the existing weakly structured information, secondly, supplement the information that is not enough to formalize the problem to known mathematical models, thirdly, declare the position of the authors a priori, mark the outline of the further presentation of the material and research limitations, which the authors limit themselves are introduced. Approaches to the analysis of theses defended in Ukraine during 1993-2020 are presented. Parameters of defended theses whose values are available in open-access databases and identifiers assigned to them are listed. An analysis of Ukrainian cities in which a large number of theses were completed and defended was carried out. Such cities are defined as scientific centers. A matrix of statistics of correlations between scientific organizations of Ukrainian cities was built. An illustration of the correlations between the cities of Ukraine is provided, and the diagram of these correlations is displayed on the map of Ukraine. In this way, the preliminary structuring of the scientific space in terms of preparation and defense of theses was carried out. Models were developed and directions for further research of the scientific space of Ukraine were determined. Based on text analysis, it is proposed to identify keywords in abstracts of theses and to determine research directions by analyzing the degree of similarity between sets of keywords related to theses. Сonclusions. Implementation of the research results will foster versatile development of the scientific space in Ukraine and strengthen its scientific potential.

Toothpaste-related interests of Google users from different countries.

The Internet is a growing source of knowledge and can provide information about oral health. This ecological study aimed to characterize the interests in toothpaste among Google users from different countries. Our hypothesis was that there would be an increase in Google users' interest in information about toothpaste. This retrospective longitudinal ecological study analyzed the toothpaste-related interest of Google users from 10 countries between January 2004 and December 2020. The monthly variation in relative search volume (RSV) and the main related queries were determined using Google Trends. Autoregressive integrated moving average (ARIMA) forecasting models were built to establish the predictive RSV values for toothpaste for an additional 12 months. Autocorrelation plots and the generalized additive model (GAM) were used to diagnose trends and seasonality in RSV curves. Additionally, the influence of social isolation related to the outbreak of COVID-19 was analyzed. Although not detected by autocorrelation function (ACF) and partial autocorrelation function (PACF) analyses, the heuristic analysis showed an increase in the interest in toothpaste-related information in all countries, with a stable trend observed in the 12-month forecasts, except for the increases in the United Kingdom and South Africa. Also, GAM analyses demonstrated a non-significant monthly or quarterly seasonal influence on data. In addition, social isolation during the COVID-19 pandemic did not influence the online information-seeking behavior of Google Search users linked to this topic. We confirmed the hypothesis that the interest of Google Search users in information about toothpaste increased in all of the 10 assessed countries.

User experience analysis while visiting selected virtual museums

The paper concerns the study of User Experience by focusing on the usability and user satisfaction aspects. The purpose of this paper was to evaluate the usability of two selected virtual museums conducted with 3 research methods: using an oculograph, a System Usability Scale (SUS) usability survey and Nielsen heuristic. The examination was conducted on the following museums that offer virtual tours: Muzeum Instrumentów Muzycznych in Poznan and Muzeum Zamojskie in Zamosc. The participants of the eye tracking experiment and the SUS survey were 22 students of Computer Science at the Politechnika Lubelska, while the Nielsen heuristics analysis was performed by 3 graduate students with relevant qualifications. The obtained eye tracking data, the results of the SUS questionnaires and the evaluation of the Nielsen heuristics were analyzed quantitatively. In addition, a qualitative analysis of eye tracking results was conducted, which provided heat maps and scanning paths. As a result, it was revealed that in the oculography method, the analyzed websites obtained comparable results. However, in the test performed with the SUS usability survey Muzeum Instrumentów Muzycznych achieved a better result. The expert team reviewing Nielsen heuristics also ranked the museum higher.

Correlations between the EMG Structure of Movement Patterns and Activity of Postural Muscles in Able-Bodied and Wheelchair Fencers.

The study involved Paralympic wheelchair fencers (N = 7) in two disability categories, and able-bodied female epee fencers (N = 7), members of the polish paralympic fencing teams. The performance of postural muscles and sword arm muscles in both groups of fencers, and of the front and rear leg muscles in the able-bodied fencers, was examined using surface electromyography with an accelerometer and the OptiTrack motion analysis system, as well as ground force reaction platforms. The activation sequence of individual muscles was determined and the structure of movement patterns in able-bodied and wheelchair fencers was formulated. A statistically significant correlation was found between the complex motor reaction time and latissimus dorsi muscle activation (p = 0.039, Z = -2.062) in wheelchair fencers. High correlations between the vertical force and EMG signal values of the gastrocnemius caput laterale muscle (0.85 for p = 0.022) were found in able-bodied fencers. A heuristic analysis indicated the significance of postural muscles in the movement patterns of wheelchair and able-bodied fencers. These muscles play a crucial role in the anticipatory postural adjustment of the trunk during technical fencing actions, including attacks on the opponent's body.

Perspectives of people with dysphagia and their supporters on the potential for 3D food printing to improve mealtime-related quality of life

Purpose To understand the views of people with dysphagia and their supporters on the feasibility of using 3D food printing to improve the visual appeal of texture-modified foods and their mealtime experiences. Materials and Methods Nine people with dysphagia and four of their supporters engaged in a virtual 3D food printing experience and interview over Zoom© about their impressions and usability of the printer and potential for it to improve their mealtime experiences. The in-depth interviews were analysed using thematic analysis and usability heuristics. Results Four content themes in the interviews impacted on the feasibility of 3D food printing. They related to the practicality, design acceptability, population suitability, and cost of 3D food printing. Usability heuristic analysis revealed that print quality, user control, error prevention, and handling of food consistencies would impact on use. Perceived low efficiency and increased time required to create food shapes meant participants were cautious in their overall impressions of the role of the device. Conclusion After an immersive virtual experience with a 3D food printer, people with dysphagia and their supporters identified a wide range of usability issues that would need to be addressed prior to implementation and in the future design of user-friendly 3D food printers for people with dysphagia. Future research should include people with dysphagia and their supporters in 3D food printer design and implementation trials. Implications for Rehabilitation 3D food printing may provide people with dysphagia who require texture-modified food a way to produce visually appealing texture-modified food if usability issues are addressed. 3D food printing could improve participation in meal preparation if the person with dysphagia chooses the food and the shape and size of the printed food shape.

Application of the DEA Method for Evaluation of Information Usefulness Efficiency on Websites

The article presents analysis and evaluation of information usefulness efficiency for recipients with secondary and higher education using the example of information and shopping websites. Different levels of quality and information usefulness efficiency have an impact on different ways of processing information by users which, in turn, may result in different consumer behavior and their decisions. The study describes the basic methodological assumptions, the research evaluation procedure of information usefulness efficiency, and the forms of informational content and various forms of information presentation and visualization, as well as the results of data analysis from the study conducted on a group of respondents. In order to determine the factors that have the greatest impact on the perception of information usefulness on websites by users, the data obtained from the study using various methods, such as online questionnaire, usability testing and heuristic analysis, were analyzed using the DEA method, which is usually used for the analysis and evaluation of efficiency. The results of the research presented in this article can be useful in creating assumptions for methods of content presentation and visualization of various forms of content building for the needs of different user groups for information and business websites.

Losing my religion: Luther, and the so‐called new perspective on Paul vis‐à‐vis feminist interpretations of Paul

AbstractThis article is based on a paper given on August 16 at the seminar “Losing my Religion” held in connection with the 14th International Congress for Luther Research: Word and World. It offers a heuristic analysis of different attacks on the reformer Martin Luther and Lutheran theology, both internally and externally, with a focus on the generalizing critique of what is at times labeled “the traditional (Protestant) confrontation perspective” by the so‐called new—or even radical—Paul New Testament scholars. It is demonstrated that the Paul readings, foremost Paul's letter to the Romans, by the new Paul perspective scholars can be aligned with the Paul readings made by radical feminist New Testament scholars, as both groups read the bible as history from an essentialist angle. By contrast, the attacked “traditional (Protestant) perspective” can be aligned with revisionist feminist New Testament scholars, as these latter groups read the bible as theology from a constructivist angle.

TALENT MANAGEMENT IN THE CONTEXT OF INDUSTRY 4.0

The purpose of the research is to study the migration of highly-qualifi ed workers of the Republic of Kazakhstan abroad during the period of intensive development of advanced technologies and to identify the links of this process with countries with a high level of industrialization. Methodology. Based on the descriptive methodology, the results of the INSEAD Research Institute (GI, GII indices) and statistical data on migration of the Republic of Kazakhstan were studied. Methods of heuristic analysis, comparison and logical justifi cation were used in the data analysis. In the course of the literature review, explanations of the basic concepts are given and the current state of talent management is determined. Based on the principles of consistency, and complex analysis the hypotheses put forward were considered. Originality/value of the research. A study was conducted based on the allocation of exclusive (elite) highly qualifi ed, talented specialists. In order to prevent the outfl ow of qualifi ed specialists who negatively aff ect the intellectual potential of the country, the comprehensive causes and consequences were explained. Findings. It is established that the outfl ow of talents abroad has no direct connection with the level of industrialization of the host country. The reason is that they cannot use high potential (HiPo) and achieve high results (HiPer) professionally. The fact that the country's level of education does not fully satisfy them also aff ects. It is proved that most often primary educational migration leads to subsequent labor migration.

Do Nudging Tendencies Depend on the Nudging Timescale Chosen in Atmospheric Models?

AbstractNudging is a ubiquitous capability of numerical weather and climate models that is widely used in a variety of applications (e.g., crude data assimilation, “intelligent” interpolation between analysis times, constraining flow in tracer advection/diffusion simulations). Here, the focus is on the momentum nudging tendencies themselves, rather than the atmospheric state that results from application of the method. The initial intent was to interpret these tendencies as a quantitative estimate of model error (net parameterization error in particular). However, it was found that nudging tendencies depend strongly on the nudging time scale chosen, which is the primary result presented here. Reducing the nudging time scale reduces the difference between the model state and the target state, but much less so than the reduction in the nudging time scale, resulting in increased nudging tendencies. The dynamical core, in particular, appears to increasingly oppose nudging tendencies as the nudging time scale is reduced. A heuristic analysis suggests such a result should be expected as long as the state the model is trying to achieve differs from the target state, regardless of the type of target state (e.g., a reanalysis, another model). These results suggest nudging tendencies cannot be quantitatively interpreted as model error. Still, two experiments aimed at seeing how nudging can identify a withheld parameterization suggest nudging tendencies do contain some information on model errors and/or missing physical processes and still might be useful in model development and tuning, even if only qualitatively.

Machine learning and Natural Language Processing of social media data for event detection in smart cities

Social media data analysis in a smart city context can represent an efficacious instrument to inform decision making. The manuscript strives to leverage the power of Natural Language Processing (NLP) techniques applied to Twitter messages using supervised learning to achieve real-time automated event detection in smart cities. A semantic-based taxonomy of risks is devised to discover and analyse associated events from data streams, with a view to: (i) read and process, in real-time, published texts (ii) classify each text into one representative real-world category (iii) assign a citizen satisfaction value to each event. To select the language processing models striking the best balance between accuracy and processing speed, we conducted a pre-emptive evaluation, comparing several baseline language models formerly employed by researchers for event classification. A heuristic analysis of several smart cities and community initiatives was conducted, with a view to define real-world scenarios as basis for determining correlations between two or more co-occurring event types and their associated levels of citizen satisfaction, while further considering environmental factors. Based on Multiple Regression Analysis (MRA), we established the relationships between scenario variables, obtaining a variance of 60%–90% between the dependent and independent variables. The selected combination of supervised NLP techniques leverages an accuracy of 88.5%. We found that all regression models had at least one variable below the 0.05 threshold of the f − t e s t , therefore at least one statistically significant independent variable. These findings ultimately illustrate how citizens, taking the role of active social sensors, can yield vital data that authorities can use to make educated decisions and sustainably construct smarter cities. • We use a bespoke taxonomy of risks to detect events on social media in a smart city. • As active social sensors, citizens provide valuable insight for smart city management. • We determine general patterns and trends in citizen sentiment in a smart city context. • Our Machine Learning techniques show an accuracy of 88.5% for risk event detection. • We validate relationships between user satisfaction and events by Regression Analysis.

Density waves in protoplanetary discs excited by eccentric planets: linear theory

ABSTRACT Spiral density waves observed in protoplanetary discs have often been used to infer the presence of embedded planets. This inference relies both on simulations as well as the linear theory of planet–disc interaction developed for planets on circular orbits to predict the morphology of the density wake. In this work, we develop and implement a linear framework for calculating the structure of the density wave in a gaseous disc driven by an eccentric planet. Our approach takes into account both the essential azimuthal and temporal periodicities of the problem, allowing us to treat any periodic perturbing potential (i.e. not only that of an eccentric planet). We test our framework by calculating the morphology of the density waves excited by an eccentric, low-mass planet embedded in a globally isothermal disc and compare our results to the recent direct numerical simulations (and heuristic wavelet analysis) of the same problem by Zhu and Zhang. We find excellent agreement with the numerical simulations, capturing all the complex eccentric features including spiral bifurcations, wave crossings, and planet-wave detachments, with improved accuracy and detail compared with the wavelet method. This illustrates the power of our linear framework in reproducing the morphology of complicated time-dependent density wakes, presenting it as a valuable tool for future studies of eccentric planet–disc interactions.

Modeling spatial spillover effect on intersection crash propensity: a case study at the county level in Ohio

The characteristics of intersection crashes are not only affected by the subject intersection where the crash occurs but also are correlated with environmental conditions of neighboring analysis zones. There are few studies on intersection crash analysis to solve certain spatial effects on microscopic safety issues by proactively incorporating highway safety improvement measures into the long-term transportation planning process. The objective of this paper is to develop a heuristic traffic safety analysis system where spatial spillovers analysis is integrated into roadway safety assessment to incorporate micro variables and macro variables. With K-means clustering technique in a GIS environment, 8 hotspot counties are identified from 88 counties in Ohio, which have high intersection crash propensity. The rest of counties are identified as general counties. Then, an innovative integrated Generalized Linear Model is adopted to identify 11 and 20 significant variables that contribute to the intersection crash propensity in hotspot counties and general counties, respectively. To verify compatibility of intersection crash frequency models with macro-level and micro-level measurement, Reading Road in Cincinnati, Hamilton County (hotspot county) and I-71 in Mason City and Lebanon City of Warren County (general county) are used as examples for the test, and the results show a good consistence.

A blockchain-based conditional privacy-preserving authentication scheme for edge computing services

Edge computing is a new paradigm, especially suitable for Internet of Things (IoT) applications, which require low latency, location awareness and mass connections. It surely makes up for some shortcomings in cloud computing in terms of energy consumption and real-time analysis areas. However, it is still a great challenge to guarantee data security and privacy protection in edge computing environment. In this paper, we focus on the topic of authentication in edge computing and propose a conditional privacy-preserving authentication scheme based on blockchain, which guarantees user anonymity, conditional traceability as well as multi-factor security. The scheme uses elliptic curve cryptography (ECC) and does not use bilinear pairing operations. The key materials are stored on the blockchain and managed by the smart contracts. Formal security proof and heuristic security analysis show that the protocol satisfies desired security requirements and resists against most kinds of conventional attacks. Moreover, the performance comparison shows that the proposed scheme is more efficient in computation and communication than previous related schemes.

Network Service Assurance and Telemetry optimisation using Heuristics

This paper identifies possible solution to reduce amount data retrieved for purpose of network monitoring, while retaining the quality of information. Legacy methods used for network service monitoring data using and data retrieved by use of synthetic tests are usually decoupled – describing different contexts. As a result, there is too many monitored operational parameters on monitored devices and consequently too much measurement data exported from network service probes. Novel approach is suggested to perform heuristic analysis of the network service configuration and decomposition of collected data at the network edge. Objective is that amount of collected data can be reduced while at the same time quality of the presented information can be improved by providing clear correlation between network service and telemetry data.

Mobile Augmented Reality Design Evaluation

Augmented reality (AR) usage has increased substantially in recent years due to the proliferation of smart phone technology. Retail companies have leveraged augmented reality by allowing users to view products in their space before they buy. Because usability is related to return on investment (Marcus, 2005; Nielsen et al., 2013), ensuring the usability of these products is vital. One way to accomplish this is through heuristic analyses (Tan et al., 2009). While there are many time-tested sets of heuristics (Nielsen, 1994; Schneiderman & Plaisant, 2004), these often fail to address many of the interactions that are unique to AR (Derby & Chaparro, 2021). In this paper, we demonstrate the viability of supplementing traditional heuristic analyses (Nielsen, 1994) with a new heuristic checklist designed specifically for augmented and mixed reality (Derby & Chaparro, In Press) by evaluating a retail AR program.

Supporting Journalistic Trust Determinations: A Heuristic Analysis of News Trust Tools Through a Transparency Lens

To combat declining trust in news in the United States, numerous tools have been created to increase transparency by providing contextual information around news content, but they have largely been developed without regard for usability. We examine 59 such tools to identify the type(s) of transparency (disclosure, participatory, or ambient) information each tool aims to provide. We then conduct a heuristic usability analysis of a subset of these transparency tools and identify common usability barriers.

Convergent Usability Evaluation of the HFES Website

The purpose of this usability analysis was to utilize convergent methods to evaluate the Human Factors and Ergonomics Society (HFES) website and provide recommendations to improve user experience. Three methods were utilized including the Mouloua and Alberti Usability Questionnaire (MAUQ), the System Usability Scale (SUS), and Net Promoter Score (NPS). An in-depth heuristic analysis using Nielsen's 10 Usability Heuristics was conducted in concordance with the previous scales to provide specific implementation recommendations for improving user experience. Our findings suggest that implementing convergent methods leads to robust knowledge about the user experience of a digital product. Both theoretical and practical implications are discussed, alongside recommendations for design improvements, and directions for future research are presented.

Residual one-dimensional convolutional neural network for neuromuscular disorder classification from needle electromyography signals with explainability

Background and ObjectiveNeuromuscular disorders are diseases that damage our ability to control body movements. Needle electromyography (nEMG) is often used to diagnose neuromuscular disorders, which is an electrophysiological test measuring electric signals generated from a muscle using an invasive needle. Characteristics of nEMG signals are manually analyzed by an electromyographer to diagnose the types of neuromuscular disorders, and this process is highly dependent on the subjective experience of the electromyographer. Contemporary computer-aided methods utilized deep learning image classification models to classify nEMG signals which are not optimized for classifying signals. Additionally, model explainability was not addressed which is crucial in medical applications. This study aims to improve prediction accuracy, inference time, and explain model predictions in nEMG neuromuscular disorder classification. MethodsThis study introduces the nEMGNet, a one-dimensional convolutional neural network with residual connections designed to extract features from raw signals with higher accuracy and faster speed compared to image classification models from previous works. Next, the divide-and-vote (DiVote) algorithm was designed to integrate each subject's heterogeneous nEMG signal data structures and to utilize muscle subtype information for higher accuracy. Finally, feature visualization was used to identify the causality of nEMGNet diagnosis predictions, to ensure that nEMGNet made predictions on valid features, not artifacts. ResultsThe proposed method was tested using 376 nEMG signals measured from 57 subjects between June 2015 to July 2020 in Seoul National University Hospital. The results from the three-class classification task demonstrated that nEMGNet's prediction accuracy of nEMG signal segments was 62.35%, and the subject diagnosis prediction accuracy of nEMGNet and the DiVote algorithm was 83.69 %, over 5-fold cross-validation. nEMGNet outperformed all models from previous works on nEMG diagnosis classification, and heuristic analysis of feature visualization results indicate that nEMGNet learned relevant nEMG signal characteristics. ConclusionsThis study introduced nEMGNet and DiVote algorithm which demonstrated fast and accurate performance in predicting neuromuscular disorders based on nEMG signals. The proposed method may be applied in medicine to support real-time electrophysiologic diagnosis.